from grid_env_ideal_obs_repeat_task import *
from grid_agent import *
from checkpoint_utils import *
from maze_factory import *
from replay_config import *
import argparse
import json
import sys
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from sklearn.manifold import TSNE
import random
from sklearn.decomposition import PCA
from matplotlib.animation import FuncAnimation
from sklearn.cluster import KMeans
import threading
from matplotlib.ticker import FuncFormatter
import mplcursors

# analysis of phase space

def progress_bar(current, total, barLength = 100):
    percent = float(current) * 100 / total
    arrow = '-' * int(percent/100 * barLength - 1) + '>'
    spaces = ' ' * (barLength - len(arrow))

    print('Progress: [%s%s] %d %%' % (arrow, spaces, percent), end='\r')
    sys.stdout.flush()

@partial(jax.jit, static_argnums=(3,))
def model_forward(variables, state, x, model):
    """ forward pass of the model
    """
    return model.apply(variables, state, x)

@jit
def get_action(y):
    return jnp.argmax(y)
get_action_vmap = jax.vmap(get_action)

# load landscape and states from file
def load_task(pth = "./logs/task.json", display = True):
    # open json file
    with open(pth, "r") as f:
        data = json.load(f)
        landscape = data["data"]
        state = data["state"]
        goal = data["goal"]
        if display:
            print("state: ", state)
            print("goal: ", goal)
            print("landscape: ", landscape)
    return landscape, state, goal

# 全局变量，用于存储和图像显示线程交互的数据
class imgview:
    global_image = None
    imgview_exit = False
    trajectory = []
    focus_i = 0
    traj_i = 0

imgview_data = imgview()

# 定义一个函数，用于在独立线程中显示图像
def show_image():
    grid_size_display = 20
    while not imgview_data.imgview_exit:
        # 检查全局变量是否有图像
        if imgview_data.global_image is not None:
            img = np.copy(imgview_data.global_image)
            state_x = imgview_data.trajectory[imgview_data.traj_i][0]
            state_y = imgview_data.trajectory[imgview_data.traj_i][1]
            cv2.circle(img, (state_y * grid_size_display + int(grid_size_display/2), state_x * grid_size_display + int(grid_size_display/2)), 7, (0, 0, 255), -1, cv2.LINE_AA)
            # 显示图像
            cv2.imshow("Image", img)
            key = cv2.waitKey(1)
            if key == ord('a'):
                imgview_data.focus_i -= 1
                print("imgview_data.focus_i: ", imgview_data.focus_i)
            elif key == ord('d'):
                imgview_data.focus_i += 1
                print("imgview_data.focus_i: ", imgview_data.focus_i)
        else:
            # 图像还未产生，等待100毫秒
            time.sleep(0.1)

def rnn_run(x, mat_intr, bias1):
    intr_vector = np.dot(x, mat_intr) + bias1
    intr_vector = np.tanh(intr_vector)
    return intr_vector

# 计算雅克比矩阵
@jax.jit
def rnn_run_vector(x, mat_intr, bias1):
    intr_vector = jnp.dot(x, mat_intr) + bias1
    intr_vector = jnp.tanh(intr_vector)
    return intr_vector - x
jacobian_fun = jax.jacrev(rnn_run_vector)
jacobian_vmap = jax.jit(jax.vmap(jacobian_fun, in_axes=(0,None,None)))

@jax.jit
def vector_field_taylor_expansion(x, x0, jacobian, mat_intr, bias1):
    vector = jnp.dot(jacobian, x - x0) + rnn_run_vector(x0, mat_intr, bias1)
    return vector
vector_field_taylor_expansion_vmap = jax.jit(jax.vmap(vector_field_taylor_expansion, in_axes=(0,0,None,None,None)))

# run linear dynamical system using vector_field_taylor_expansion
def run_LDS_one_step(x, x0, dt, jacobian, mat_intr, bias1):

    x_dot = vector_field_taylor_expansion(x, x0, jacobian, mat_intr, bias1)
    x1 = x + x_dot * dt

    return x1.copy()

def compute_linearized_err_1X(sample_point, mat_intr, bias1):

    jacobian_of_x = jacobian_fun(jnp.array(sample_point), mat_intr, bias1)

    # 计算 jacobian_of_x 的所有特征值
    eigenvalues = np.linalg.eigvals(jacobian_of_x)
    # 对 eigenvalues 按照实部从大到小进行排序
    eigenvalues = np.sort(eigenvalues)[::-1]
    print("sorted eigenvalues of jacobian_of_x: ", eigenvalues)

    # 判断 eigenvalues 中是否含有实部大于零的元素
    if any(np.real(eigenvalues) > 0):
        print("存在实部大于零的项")
    else:
        print("不存在实部大于零的项")

    # 将 eigenvalues 所有实部绘制成 barchart
    fig, ax = plt.subplots()
    ax.bar(np.arange(len(eigenvalues)), np.real(eigenvalues))
    plt.show()

    sample_vector = rnn_run_vector(sample_point, mat_intr, bias1)
    sample_vector_len = np.linalg.norm(sample_vector)

    initial_state = sample_point
    current_state = initial_state.copy()
    current_state0 = current_state.copy()
    traj_len = 0

    next_state = rnn_run(initial_state, mat_intr, bias1)

    # while traj_len < sample_vector_len:
    for i in range(2000):
        current_state0 = current_state.copy()
        current_state = run_LDS_one_step(current_state, initial_state, 0.01, jacobian_of_x, mat_intr, bias1)
        delta_state = current_state - initial_state
        delta_state_len = np.linalg.norm(delta_state)
        traj_len += delta_state_len
        current_state0 = current_state.copy()

    diff_next_state_current_state = np.linalg.norm(next_state - current_state0)
    pears_corr = np.corrcoef(next_state, current_state0)[0, 1]

    current_state0_dot = vector_field_taylor_expansion(current_state0, initial_state, jacobian_of_x, mat_intr, bias1)
    next_state_vector = rnn_run_vector(next_state, mat_intr, bias1)

    pears_corr_vector = np.corrcoef(current_state0_dot, next_state_vector)[0, 1]

    print("pears_corr_vector: ", pears_corr_vector)

    return diff_next_state_current_state, pears_corr, current_state0, next_state

def ivf():

    """ parse arguments
    """
    rpl_config = ReplayConfig()

    parser = argparse.ArgumentParser()
    parser.add_argument("--model_pth", type=str, default=rpl_config.model_pth)
    parser.add_argument("--map_size", type=int, default=rpl_config.map_size)
    parser.add_argument("--task_pth", type=str, default=rpl_config.task_pth)
    parser.add_argument("--log_pth", type=str, default=rpl_config.log_pth)
    parser.add_argument("--nn_size", type=int, default=rpl_config.nn_size)
    parser.add_argument("--nn_type", type=str, default=rpl_config.nn_type)
    parser.add_argument("--show_kf", type=str, default=rpl_config.show_kf)
    parser.add_argument("--visualization", type=str, default=rpl_config.visualization)
    parser.add_argument("--video_output", type=str, default=rpl_config.video_output)
    parser.add_argument("--life_duration", type=int, default=rpl_config.life_duration)
    parser.add_argument("--start_i", type=int, default=rpl_config.start_i)
    parser.add_argument("--end_i", type=int, default=rpl_config.end_i)
    parser.add_argument("--load_data", type=int, default=rpl_config.load_data)

    args = parser.parse_args()

    rpl_config.model_pth = args.model_pth
    rpl_config.map_size = args.map_size
    rpl_config.task_pth = args.task_pth
    rpl_config.log_pth = args.log_pth
    rpl_config.nn_size = args.nn_size
    rpl_config.nn_type = args.nn_type
    rpl_config.show_kf = args.show_kf
    rpl_config.visualization = args.visualization
    rpl_config.video_output = args.video_output
    rpl_config.life_duration = args.life_duration
    rpl_config.start_i = args.start_i
    rpl_config.end_i = args.end_i
    rpl_config.load_data = args.load_data

    if rpl_config.load_data == 0:

        """ load model
        """
        params = load_weights(rpl_config.model_pth)
        # 定义一个函数，用于生成随机权重
        def init_weights_r(key, shape):
            return jax.random.normal(key, shape)
        # 生成一个随机的 PRNGKey
        key = jax.random.PRNGKey(np.random.randint(0, 1000))
        # key = jax.random.PRNGKey(4512)
        # 使用 tree_map 遍历 params 对象，并使用 init_weights 函数生成随机权重
        random_params = jax.tree_map(lambda x: init_weights_r(key, x.shape), params)
        # params = random_params

        # get elements of params
        tree_leaves = jax.tree_util.tree_leaves(params)
        for i in range(len(tree_leaves)):
            print("shape of leaf ", i, ": ", tree_leaves[i].shape)

        bias1 = np.array(tree_leaves[0])
        mat1 = np.array(tree_leaves[1])
        print("mat1.shape: ", mat1.shape)
        print("bias1: ", bias1)

        mat_obs = np.array(tree_leaves[1])[rpl_config.nn_size:rpl_config.nn_size+10,:]
        mat_intr = np.array(tree_leaves[1])[0:rpl_config.nn_size,:]
        print("mat_obs.shape: ", mat_obs.shape)
        
        """ create agent
        """
        if rpl_config.nn_type == "vanilla":
            model = RNN(hidden_dims = rpl_config.nn_size)
        elif rpl_config.nn_type == "gru":
            model = GRU(hidden_dims = rpl_config.nn_size)

        # check if param fits the agent
        if rpl_config.nn_type == "vanilla":
            assert params["params"]["Dense_0"]["kernel"].shape[0] == rpl_config.nn_size + 10

        n_samples = 50
        k1 = npr.randint(0, 1000000)
        rnn_state = model.initial_state_rnd(n_samples, k1)
        rnn_state_old = rnn_state.copy()
        diff = jnp.abs(rnn_state - rnn_state_old)
        rnn_state_old = rnn_state.copy()
        diff_norm = jnp.linalg.norm(diff, axis=1)
        diff_norm_old = diff_norm.copy()
        norm_std = diff_norm.copy()

        rnn_state_init = rnn_state.copy()

        obs_zero = jnp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0] for i in range(n_samples)])

        rnn_state_trajectory = []
        intr_field = []

        for t in range(rpl_config.life_duration):

            progress_bar(t, rpl_config.life_duration)

            intr_vector = np.dot(rnn_state, mat_intr) + bias1
            intr_vector = np.tanh(intr_vector)
            intr_field.append(intr_vector - rnn_state)

            rnn_state_trajectory.append(np.array(rnn_state).copy())
            
            """ model forward 
            """
            # rnn_state, y1 = model_forward(params, rnn_state, obs_zero, model)
            rnn_state = intr_vector
            
        print(rnn_state.shape)
        print(norm_std.shape)
        rnn_state_np = np.array(rnn_state)

        # 将 rnn_state_trajectory 展开成 rnn_state_np 的形状
        rnn_state_trajectory_np = np.array(rnn_state_trajectory)
        rnn_state_trajectory_np = rnn_state_trajectory_np.reshape(-1, rnn_state_trajectory_np.shape[-1])
        print("shape of rnn_state_trajectory_np: ", rnn_state_trajectory_np.shape)

        # 将 intr_field 展开成 rnn_state_np 的形状
        intr_field_np = np.array(intr_field)
        intr_field_np = intr_field_np.reshape(-1, intr_field_np.shape[-1])
        print("shape of intr_field_np: ", intr_field_np.shape)

        # 对 rnn_state_np 进行 PCA
        intrinsic_pca = PCA()
        intrinsic_pca.fit(rnn_state_trajectory_np)
        rnn_state_trajectory_np_pca = intrinsic_pca.transform(rnn_state_trajectory_np)

        intr_field_np_pca = intrinsic_pca.transform(intr_field_np + intrinsic_pca.mean_)

        # 保存数据
        np.save("./logs/rnn_state_trajectory_np_pca.npy", rnn_state_trajectory_np_pca)
        np.save("./logs/rnn_state_trajectory_np.npy", rnn_state_trajectory_np)
        np.save("./logs/intr_field_np_pca.npy", intr_field_np_pca)
    
    elif rpl_config.load_data == 1:

        """ load model
        """
        params = load_weights(rpl_config.model_pth)
        # 定义一个函数，用于生成随机权重
        def init_weights_r(key, shape):
            return jax.random.normal(key, shape)
        # 生成一个随机的 PRNGKey
        key = jax.random.PRNGKey(np.random.randint(0, 1000))
        # key = jax.random.PRNGKey(4512)
        # 使用 tree_map 遍历 params 对象，并使用 init_weights 函数生成随机权重
        random_params = jax.tree_map(lambda x: init_weights_r(key, x.shape), params)
        # params = random_params

        # get elements of params
        tree_leaves = jax.tree_util.tree_leaves(params)
        for i in range(len(tree_leaves)):
            print("shape of leaf ", i, ": ", tree_leaves[i].shape)

        bias1 = np.array(tree_leaves[0])
        mat1 = np.array(tree_leaves[1])
        print("mat1.shape: ", mat1.shape)
        print("bias1: ", bias1)

        mat_obs = np.array(tree_leaves[1])[rpl_config.nn_size:rpl_config.nn_size+10,:]
        mat_intr = np.array(tree_leaves[1])[0:rpl_config.nn_size,:]
        print("mat_obs.shape: ", mat_obs.shape)

        rnn_state_trajectory_np_pca = np.load("./logs/rnn_state_trajectory_np_pca.npy")
        rnn_state_trajectory_np = np.load("./logs/rnn_state_trajectory_np.npy")
        intr_field_np_pca = np.load("./logs/intr_field_np_pca.npy")

        intrinsic_pca = PCA()
        intrinsic_pca.fit(rnn_state_trajectory_np)

    # # 740 is inverse flow
    # dim0 = 0
    # dim1 = 1
    # dim2 = 2
    # fig = plt.figure()
    # ax = fig.add_subplot(111, projection='3d')
    # ax.scatter(np.array(rnn_state_trajectory_np_pca)[:, dim0], 
    #             np.array(rnn_state_trajectory_np_pca)[:, dim1], 
    #             np.array(rnn_state_trajectory_np_pca)[:, dim2],
    #             s = 10
    #             )
    # # ax.quiver(rnn_state_trajectory_np_pca[:, dim0], rnn_state_trajectory_np_pca[:, dim1], rnn_state_trajectory_np_pca[:, dim2], 
    # #                 intr_field_np_pca[:, dim0], intr_field_np_pca[:, dim1], intr_field_np_pca[:, dim2], color='r', length=0.2, arrow_length_ratio=0.3)
    # for i in range(rnn_state_trajectory_np_pca.shape[0] - 1):
    #     ax.quiver(rnn_state_trajectory_np_pca[i, dim0], rnn_state_trajectory_np_pca[i, dim1], rnn_state_trajectory_np_pca[i, dim2], 
    #                 intr_field_np_pca[i, dim0], intr_field_np_pca[i, dim1], intr_field_np_pca[i, dim2], color='r', length=0.2, arrow_length_ratio=0.3)
    # # cursor1 = mplcursors.cursor(ax, hover=True)
    # # @cursor1.connect("add")
    # # def on_add(sel):
    # #     index = sel.target.index
    # #     sel.annotation.set_alpha(0.9)
    # #     sel.annotation.set_text(str(index))
    # plt.show()

    # 随机化 sample_id
    sample_id = np.random.randint(0, rnn_state_trajectory_np.shape[0])
    sample_id = 740

    norm_err, corr, current_state0, next_state = compute_linearized_err_1X(rnn_state_trajectory_np[sample_id], mat_intr, bias1)
    print("norm_err: ", norm_err)
    print("pears corr: ", corr)
    # print("pears corr vector: ", vector_corr)

    # # 将 next_state 和 current_state 绘制成两个柱状图，作为两个独立的子图，显示在同一个窗口中
    # fig, ax = plt.subplots(1, 2, figsize=(10, 5))
    # ax[0].bar(np.arange(0, next_state.shape[0]), next_state, color='r')
    # ax[1].bar(np.arange(0, current_state0.shape[0]), current_state0, color='g')
    # plt.show()

    next_state_pca = intrinsic_pca.transform(next_state.reshape(1, -1))
    current_state0_pca = intrinsic_pca.transform(current_state0.reshape(1, -1))
    
    dim1 = 0
    dim2 = 1
    dim3 = 2
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    ax.scatter(np.array(rnn_state_trajectory_np_pca)[:, dim1], 
            np.array(rnn_state_trajectory_np_pca)[:, dim2], 
            np.array(rnn_state_trajectory_np_pca)[:, dim3]
            )
    ax.plot(np.array(rnn_state_trajectory_np_pca)[sample_id, dim1],
            np.array(rnn_state_trajectory_np_pca)[sample_id, dim2],
            np.array(rnn_state_trajectory_np_pca)[sample_id, dim3],
            color='r', marker='o', markersize=20)
    
    # ax.plot(next_state_pca[0,dim1], next_state_pca[0,dim2], next_state_pca[0,dim3], color='g', marker='o', markersize=20)
    ax.plot(current_state0_pca[0,dim1], current_state0_pca[0,dim2], current_state0_pca[0,dim3], color='b', marker='o', markersize=20)
    
    cursor1 = mplcursors.cursor(ax, hover=True)
    @cursor1.connect("add")
    def on_add(sel):
        index = sel.target.index
        sel.annotation.set_alpha(0.9)
        sel.annotation.set_text(str(index))
    
    plt.show()

if __name__ == "__main__":
    
    ivf()